{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello World'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def hello():\n",
    "    return \"Hello World\"\n",
    "\n",
    "hello()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f391a684400>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Preejk/L53jVTw46TFCp6Eemztlcf52sPr2F8Xi73fGFGrz/VQVtS81clItKOQzUNfOnB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f391a6a8470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.DataFrame([1,2,4,8,16,32,64,128,256])\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>56.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>85.416301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>64.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>256.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0\n",
       "count    9.000000\n",
       "mean    56.777778\n",
       "std     85.416301\n",
       "min      1.000000\n",
       "25%      4.000000\n",
       "50%     16.000000\n",
       "75%     64.000000\n",
       "max    256.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Markdownテスト\n",
    "\n",
    "## リストテスト\n",
    "\n",
    "* リスト1\n",
    "* リスト2\n",
    "\n",
    "## 数式のテスト\n",
    "\n",
    "$ \\sqrt{x^2 + y^2} $"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\n",
    "\\color{red}{\n",
    "  \\Rule2mm2mm0mm\n",
    "  \\Rule6mm4mm0mm\n",
    "  \\mathbb{M}\n",
    "  \\Rule6mm2mm0mm\n",
    "}\n",
    "\\color{lightyellow}{\\hspace-18mm{\\lower6mm{\n",
    "  \\Rule2mm4mm0mm\n",
    "  \\Rule4mm0mm4mm\n",
    "  \\Rule8mm6mm4mm\n",
    "  \\Rule2mm4mm4mm\n",
    "  \\Rule2mm4mm2mm\n",
    "  \\Rule2mm2mm0mm\n",
    "}}}\n",
    "\\color{gray}{\\hspace-22mm{\\lower4mm{\n",
    "  \\Rule2mm2mm2mm\n",
    "  \\Rule2mm4mm4mm\n",
    "  \\Rule4mm4mm2mm\n",
    "  \\Space4mm0mm0mm\n",
    "  \\Rule2mm4mm0mm\n",
    "  \\Rule2mm0mm4mm\n",
    "  \\lower2mm{\\Rule4mm0mm2mm}\n",
    "}}}\n",
    "\\lower8mm{\\verb|  < Here we go!|}\n",
    "\\tag{1}\n",
    "$"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
